Today, many personal electronic devices come equipped with digital cameras. Often, these devices perform many functions, and, as a consequence, the digital image sensors included in these devices must often be smaller than sensors in conventional cameras. Further, the camera hardware in these devices often lacks sophisticated features sometimes found in larger, professional-style conventional cameras such as manual exposure controls and manual focus. Thus, it is important that digital cameras in personal electronic devices be able to produce the most visually appealing images in a wide variety of lighting and scene situations with limited or no interaction from the user, as well as in the most computationally and cost effective manner.
One feature that has been implemented in some digital cameras to create visually appealing images is known as auto exposure. Auto exposure can be defined generally as any algorithm that automatically calculates and/or manipulates certain camera exposure parameters, e.g., exposure time, gain, or f-number, in such a way that the currently exposed scene is captured in a desirable manner. For example, there may be a predetermined optimum brightness value for a given scene that the camera will try to achieve by adjusting the camera's exposure value. Exposure value (EV) can be defined generally as: log2 N2/t, wherein N is the relative aperture (f-number), and t is the exposure time (i.e., “shutter speed”) expressed in seconds. Some auto exposure algorithms calculate and/or manipulate the exposure parameters such that a mean, center-weighted mean, median, or more complicated weighted value (as in matrix-metering) of the image's brightness will equal a predetermined optimum brightness value in the resultant, auto exposed scene.
Auto exposure algorithms may also be aided by face detection technologies. In these auto exposure algorithms, the camera will attempt to locate one or more human faces within the scene and tailor its exposure and/or focus parameters to the location of the face or faces in the scene. Such algorithms account for the fact that a good assumption in most consumer photography is that human faces are often the desired subject in an image and, thus, focusing on and exposing properly such faces will often lead to visually pleasing images.
Auto exposure algorithms would also benefit from intelligently and continuously, i.e., dynamically, adjusting exposure parameters in response to changes in the scene being displayed, e.g., on the camera's preview screen. Often, the face detection, auto focusing, and/or auto exposure decisions made by the camera are “locked in” at some point in time before the image is taken. In some cameras, this is done via a user-actuated mechanism, e.g., pressing the shutter button halfway down to lock in the focus and exposure parameters, and then depressing the shutter button fully to take the image according to the locked-in focus and exposure parameters. In other cameras, parameters are set based on the previously taken image. This sort of static lock-in of exposure parameters can make it difficult to tell if a scene will be properly exposed or is being properly exposed in situations where the objects in the scene are moving or the lighting level of the scene is changing. Further, auto exposed video recording applications are not possible with a static face detection-assisted auto exposure algorithm.
Thus, there is need for a system, computer readable medium, and method for intelligently and dynamically setting a camera's exposure parameters in a visually pleasing way based at least in part on face detection.